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Technology
Machine Learning Street Talk (MLST)
by Machine Learning Street Talk (MLST)
Today: #115 in Technology on our combined chart.
Today: #120 in Technology, United Kingdom on our combined chart.
Today: #88 in Technology, Canada on our combined chart.
Today: #104 in Technology, Australia on our combined chart.
Today: #195 in Technology, Germany on our combined chart.
Today: #181 in Technology, Brazil on our combined chart.
Today: #188 in Technology, Mexico on our combined chart.
Today: #37 in Technology, Ireland on our combined chart.
Today: #50 in Technology, India on our combined chart.
Today: #182 in Technology, South Africa on our combined chart.
Chart positions today · United States
Not on the New Zealand charts we track today.
Not on the Japan charts we track today.
Not on the Philippines charts we track today.
If you like Machine Learning Street Talk (MLST), try…
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Ranking history · United States
Latest daily rankings: Oct 27. New rankings will appear after the next update.
Position on the Technology combined chart over the last 14 days. Toggle platforms to compare their positions. Lower rank numbers are better.
Episode releases
View as table
| Date | Combined Rank | Apple Podcasts | Castbox |
|---|---|---|---|
| Oct 27 | #115 | #102 | — |
| Oct 26 | #140 | #134 | — |
| Oct 25 | #168 | #165 | — |
| Oct 24 | #176 | #172 | — |
| Oct 23 | #169 | #165 | — |
| Oct 22 | #129 | #117 | — |
| Oct 21 | #160 | #155 | — |
| Oct 20 | #158 | #152 | — |
| Oct 19 | #123 | #112 | — |
| Oct 18 | #126 | #115 | — |
| Oct 17 | #107 | #92 | — |
| Oct 16 | #124 | #115 | — |
| Oct 15 | #121 | #110 | — |
| Oct 14 | #112 | #102 | #98 |
Latest episodes
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Mathematics Just Changed Forever — Kevin Hartnett
October 26, 2026 · 1 hr 34 minKevin Hartnett is the author of *The Proof in the Code*.SPONSOR:---Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open.Apply now: https://cyber.fund---In 1998…
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What Most People Get Wrong About Evolution | Akarsh Kumar
October 10, 2026 · 48 minA lot of people in AI treat evolution as a dumb fallback, basically random search for when you can't take a gradient. Akarsh Kumar thinks that is wrong. Selection hangs on to partial solutions, so mutations only need to be useful about 1%…
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How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen
October 1, 2026 · 1 hr 10 minTsung-Hsien (Shawn) Wen, CTO of PolyAI, tells Tim Scarfe why voice agents are harder than text agents. Voice adds time, and a good conversation depends on adapting to the person on the line, not just on reasoning to the best answer. Shawn…
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Who Checks a Proof No Human Can Read? — Leo de Moura
September 30, 2026 · 1 hr 14 minLeonardo de Moura created Lean and co-created Z3. ---This episode is sponsored by Parallel.Parallel, where agents find answers: web search, extraction and deep research APIs built for AI agents.Start free with the Parallel MCP server and…
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When AI Research Starts Moving Faster Than Human Research - Zhengyao Jiang
September 26, 2026 · 44 minWeco let an AI coding agent rewrite the harness around another agent for eight days: its code, prompts and tools, while the underlying language model stayed fixed. Tim Scarfe asks Weco co-founder Zhengyao Jiang what the reported gains over…
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How Deep Learning Finally Cracked Messy Tables - Frank Hutter
September 23, 2026 · 1 hr 53 minFrank Hutter, co-founder of Prior Labs, talks about TabPFN, a tabular foundation model that makes predictions in a single forward pass, and the research behind it. TabPFN is pre-trained on synthetic datasets drawn from a prior over…
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Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick
September 21, 2026 · 2 hr 14 minAlexander Mattick is a researcher at Fraunhofer IIS and a PhD researcher at the University of Technology Nuremberg (UTN), and a regular on Yannic Kilcher's Discord. He first came on MLST in 2022, after helping research the Yann LeCun and…
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How Physical AI Learns Across Language, Video and Action — Ming-Yu Liu
September 15, 2026 · 26 minThe car making a left turn at the start of this episode was never filmed. Cosmos 3 generated it. Ming-Yu Liu, who leads the Cosmos research at NVIDIA, explains how one model can describe a video, generate one, and produce robot…
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Speech Recognition Is Not a Solved Problem — Pavan Kumar Reddy
September 14, 2026 · 1 hr 42 minPavan Kumar Reddy leads audio research at Mistral AI. He joins Tim Scarfe for a deep technical tour of Voxtral — and explains why the frontier of deployed voice is still a cascade of specialised models rather than one end-to-end…
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How Replication Could Teach Machines What Good Science Looks Like — Edward Hughes
September 11, 2026 · 2 hr 2 minCan a machine learn the judgement that separates a plausible-looking result from a faithful experiment? Edward Hughes, Chief Scientist and co-founder of Inherent, joins Tim Scarfe to argue that creativity is not optimisation, and that the…
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AI 2040: Plan A report - Daniel Kokotajlo & Thomas Larsen
September 8, 2026 · 1 hr 30 minCould slowing AI development make superintelligence safer? Daniel Kokotajlo and Thomas Larsen of the AI Futures Project join Tim Scarfe to examine AI 2040: Plan A, a proposal to buy time before AI exceeds human control. SPONSOR: --- Cyber…
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Designing How AI Grows — Tom McGrath
September 2, 2026 · 1 hr 40 minTom McGrath is co-founder and Chief Scientist at Goodfire, and a former Google DeepMind researcher. He joins Tim Scarfe to ask what neural networks actually learn, whether their internal representations converge on structures in the world…
About the show
Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).